A texture–based weed classification method was developed. The method consisted of a low–level Gabor
wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This
classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass
categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur,
velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied.
After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of
classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical
implementation under real–time constraints.